An all-Africa dataset of energy model “supply regions” for solar photovoltaic and wind power

With solar and wind power generation reaching unprecedented growth rates globally, much research effort has recently gone into a comprehensive mapping of the worldwide potential of these variable renewable electricity (VRE) sources. From a perspective of energy systems analysis, the locations with the strongest resources may not necessarily be the best candidates for investment in new power plants, since the distance from existing grid and road infrastructures and the temporal variability of power generation also matter. To inform energy planning and policymaking, cost-optimisation models for energy systems must be fed with adequate data on potential sites for VRE plants, including costs reflective of resource strength, grid expansion needs and full hourly generation profiles. Such data, tailored to energy system models, has been lacking up to now. In this study, we present a new open-source and open-access all-Africa dataset of “supply regions” for solar photovoltaic and onshore wind power to feed energy models and inform capacity expansion planning.


A. Full list of metadata provided with MSRs
A full list of the metadata provided for all MSRs in the data files is shown in Table 1 for solar PV and in Table 2 for wind power.

B. Costs used for LCOE calculations
The levelized cost of electricity (LCOE) of solar PV and wind power plants deployed in the identified MSRs is composed of three separate terms: Where refers to the investment and operation & maintenance (O&M) costs of the power plant itself; refers to the costs for transmission infrastructure, and refers to the costs for road infrastructure. Each separate LCOE term is calculated as follows: where represents the year of the asset's lifetime (0 ≤ ≤ , with the plant's lifetime), are the initial (overnight) costs related to construction of the asset in each year , are the operational and maintenance costs in each year , is the total electricity generated by the plant in each year , and is the discount rate. C. LCOE versus capacity factor: full plots Figure 1 and Figure 2 display the full scatterplots of MSR LCOE versus average capacity factor. Each point represents an MSR; point sizes are proportional to MSR sizes (area, or equivalently the maximum deployable capacity in MW in each MSR). Colours represent the "power pool" that the country in question belongs to; this classification is given in Table 4. .

D. Effect of future CAPEX and OPEX reductions
Given the substantial reductions in capital and operational expenses (CAPEX and OPEX) for solar PV and wind power expected over the coming years, the results of this study-in particular, the compromise between exploiting good resources and paying the "remoteness premium" when attempting to screen the lowest-cost sites-may shift in the future. Assuming no changes in infrastructure costs for transmission lines, substations, and road construction, a reduction in CAPEX and OPEX of VRE would theoretically tend to shift the most favourable MSRs (in LCOE terms) somewhat closer to grid infrastructures, with the avoided remoteness premium making up for losses in average yield. The question is whether this effect is substantial, marginal, or non-existent.
A sensitivity test was run on the basis of predicted CAPEX and OPEX values for 2040 for solar PV and wind. Based on historical learning rates observed for these sources 4 , for this test, CAPEX were assumed to drop by 50% between the present-day and 2040 for both solar PV and wind, and OPEX were assumed to drop by 50% for solar PV and by 60% for wind. The results are summarised in Table 5, which shows the average gains in grid closeness of MSRs (when passing from present-day cost assumptions to 2040 cost assumptions), alongside the corresponding average compromises in CFs. This is done both for the African average, as well as for the three countries with the strongest geographical shift in MSRs when passing from present-day to 2040 costs. It can be concluded that the effect, on average, is relatively small for solar PV, whose MSRs already tend to cluster around grid infrastructure even under present-day costs, an effect which would be only slightly strengthened by further cost drops. The effect is more important for wind, although strongly diverging on a country-by-country basis, with some countries seeing nearly no geographical shift of MSRs and others seeing substantial changes. A few clear outliers can be identified for wind power: these are countries straddling the Sahelian belt, which has excellent wind resources but mostly at several hundreds of kilometres from existing grid infrastructure.

E. Clustering approach
The clustering approach developed to complement the MSR identification algorithm is described in Methods. In Figure 3, we show an example of the results of the clustering in a geographical sense. We use the example of Mali, which had 145 separate MSRs for solar PV and 131 separate MSRs for wind (based on the screening criterion of maximum 5% coverage of a country area mentioned in the main text). Here, the MSRs were grouped into five clusters for both solar PV and wind, each with their own maximum deployable capacity, weighted average cost parameters and weighted average capacity factor time series.